Overview

Dataset statistics

Number of variables11
Number of observations756
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory108.7 KiB
Average record size in memory147.2 B

Variable types

Categorical1
Numeric10

Alerts

date has a high cardinality: 756 distinct valuesHigh cardinality
oil_brent is highly overall correlated with oil_dubai and 7 other fieldsHigh correlation
oil_dubai is highly overall correlated with oil_brent and 7 other fieldsHigh correlation
coffee_arabica is highly overall correlated with oil_brent and 7 other fieldsHigh correlation
coffee_robustas is highly overall correlated with coffee_arabica and 2 other fieldsHigh correlation
tea_columbo is highly overall correlated with oil_brent and 6 other fieldsHigh correlation
tea_kolkata is highly overall correlated with oil_brent and 7 other fieldsHigh correlation
tea_mombasa is highly overall correlated with oil_brent and 7 other fieldsHigh correlation
sugar_eu is highly overall correlated with oil_brent and 1 other fieldsHigh correlation
sugar_us is highly overall correlated with oil_brent and 6 other fieldsHigh correlation
sugar_world is highly overall correlated with oil_brent and 6 other fieldsHigh correlation
date is uniformly distributedUniform
date has unique valuesUnique

Reproduction

Analysis started2023-11-15 21:49:56.111161
Analysis finished2023-11-15 21:50:26.651759
Duration30.54 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct756
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
1960-01-01
 
1
2002-05-01
 
1
2001-08-01
 
1
2001-09-01
 
1
2001-10-01
 
1
Other values (751)
751 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7560
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique756 ?
Unique (%)100.0%

Sample

1st row1960-01-01
2nd row1960-02-01
3rd row1960-03-01
4th row1960-04-01
5th row1960-05-01

Common Values

ValueCountFrequency (%)
1960-01-01 1
 
0.1%
2002-05-01 1
 
0.1%
2001-08-01 1
 
0.1%
2001-09-01 1
 
0.1%
2001-10-01 1
 
0.1%
2001-11-01 1
 
0.1%
2001-12-01 1
 
0.1%
2002-01-01 1
 
0.1%
2002-02-01 1
 
0.1%
2002-03-01 1
 
0.1%
Other values (746) 746
98.7%

Length

2023-11-15T22:50:26.870625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1960-01-01 1
 
0.1%
1960-11-01 1
 
0.1%
1961-10-01 1
 
0.1%
1960-03-01 1
 
0.1%
1960-04-01 1
 
0.1%
1960-05-01 1
 
0.1%
1960-06-01 1
 
0.1%
1960-07-01 1
 
0.1%
1960-08-01 1
 
0.1%
1960-09-01 1
 
0.1%
Other values (746) 746
98.7%

Most occurring characters

ValueCountFrequency (%)
0 1866
24.7%
1 1755
23.2%
- 1512
20.0%
9 735
 
9.7%
2 522
 
6.9%
6 255
 
3.4%
8 255
 
3.4%
7 255
 
3.4%
5 135
 
1.8%
3 135
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6048
80.0%
Dash Punctuation 1512
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1866
30.9%
1 1755
29.0%
9 735
 
12.2%
2 522
 
8.6%
6 255
 
4.2%
8 255
 
4.2%
7 255
 
4.2%
5 135
 
2.2%
3 135
 
2.2%
4 135
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 1512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1866
24.7%
1 1755
23.2%
- 1512
20.0%
9 735
 
9.7%
2 522
 
6.9%
6 255
 
3.4%
8 255
 
3.4%
7 255
 
3.4%
5 135
 
1.8%
3 135
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1866
24.7%
1 1755
23.2%
- 1512
20.0%
9 735
 
9.7%
2 522
 
6.9%
6 255
 
3.4%
8 255
 
3.4%
7 255
 
3.4%
5 135
 
1.8%
3 135
 
1.8%

oil_brent
Real number (ℝ)

Distinct528
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.724944
Minimum1.21
Maximum133.87304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:27.131461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.33
Q110.564999
median20.48913
Q347.1575
95-th percentile108.25964
Maximum133.87304
Range132.66304
Interquartile range (IQR)36.592501

Descriptive statistics

Standard deviation31.885368
Coefficient of variation (CV)0.97434447
Kurtosis0.59426373
Mean32.724944
Median Absolute Deviation (MAD)18.40913
Skewness1.203709
Sum24740.058
Variance1016.6767
MonotonicityNot monotonic
2023-11-15T22:50:27.433279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.630000114 12
 
1.6%
1.36 12
 
1.6%
1.57 12
 
1.6%
1.21 12
 
1.6%
1.32 12
 
1.6%
1.33 12
 
1.6%
1.27 12
 
1.6%
1.42 12
 
1.6%
1.45 12
 
1.6%
1.5 12
 
1.6%
Other values (518) 636
84.1%
ValueCountFrequency (%)
1.21 12
1.6%
1.27 12
1.6%
1.32 12
1.6%
1.33 12
1.6%
1.36 12
1.6%
1.42 12
1.6%
1.45 12
1.6%
1.5 12
1.6%
1.52 12
1.6%
1.57 12
1.6%
ValueCountFrequency (%)
133.8730435 1
0.1%
133.0485714 1
0.1%
124.9286364 1
0.1%
123.9361905 1
0.1%
123.07 1
0.1%
120.4635 1
0.1%
120.08 1
0.1%
119.702381 1
0.1%
116.5195 1
0.1%
116.46 1
0.1%

oil_dubai
Real number (ℝ)

Distinct524
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.23813
Minimum1.21
Maximum131.22478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:27.734092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.33
Q110.4525
median18.55
Q345.576023
95-th percentile104.96917
Maximum131.22478
Range130.01478
Interquartile range (IQR)35.123523

Descriptive statistics

Standard deviation30.936611
Coefficient of variation (CV)0.99034774
Kurtosis0.62002136
Mean31.23813
Median Absolute Deviation (MAD)16.68
Skewness1.2311269
Sum23616.026
Variance957.07392
MonotonicityNot monotonic
2023-11-15T22:50:28.028907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.630000114 12
 
1.6%
1.57 12
 
1.6%
1.52 12
 
1.6%
1.5 12
 
1.6%
1.45 12
 
1.6%
1.42 12
 
1.6%
1.36 12
 
1.6%
1.33 12
 
1.6%
1.32 12
 
1.6%
1.27 12
 
1.6%
Other values (514) 636
84.1%
ValueCountFrequency (%)
1.21 12
1.6%
1.27 12
1.6%
1.32 12
1.6%
1.33 12
1.6%
1.36 12
1.6%
1.42 12
1.6%
1.45 12
1.6%
1.5 12
1.6%
1.52 12
1.6%
1.57 12
1.6%
ValueCountFrequency (%)
131.2247826 1
0.1%
127.587619 1
0.1%
122.2759091 1
0.1%
118.9486364 1
0.1%
117.25 1
0.1%
116.1461905 1
0.1%
115.73 1
0.1%
115.7 1
0.1%
113.2109524 1
0.1%
113.11 1
0.1%

coffee_arabica
Real number (ℝ)

Distinct733
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5765548
Minimum0.7776
Maximum7.0036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:28.315732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.7776
5-th percentile0.854325
Q11.351625
median2.6977944
Q33.31295
95-th percentile4.97505
Maximum7.0036
Range6.226
Interquartile range (IQR)1.961325

Descriptive statistics

Standard deviation1.3424536
Coefficient of variation (CV)0.52102659
Kurtosis-0.10152662
Mean2.5765548
Median Absolute Deviation (MAD)1.03925
Skewness0.59039769
Sum1947.8754
Variance1.8021816
MonotonicityNot monotonic
2023-11-15T22:50:28.599555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8891 3
 
0.4%
2.9427 2
 
0.3%
0.8929 2
 
0.3%
2.976898386 2
 
0.3%
0.8267 2
 
0.3%
3.10630958 2
 
0.3%
0.9026 2
 
0.3%
0.8109 2
 
0.3%
0.8047 2
 
0.3%
0.8256 2
 
0.3%
Other values (723) 735
97.2%
ValueCountFrequency (%)
0.7776 1
0.1%
0.795 1
0.1%
0.797 1
0.1%
0.7992 1
0.1%
0.7998 1
0.1%
0.802 1
0.1%
0.8031 1
0.1%
0.8042 1
0.1%
0.8047 2
0.3%
0.8064 1
0.1%
ValueCountFrequency (%)
7.0036 1
0.1%
6.7058 1
0.1%
6.616505544 1
0.1%
6.439033634 1
0.1%
6.417428358 1
0.1%
6.346880518 1
0.1%
6.2889 1
0.1%
6.169188146 1
0.1%
6.062264076 1
0.1%
6.060059456 1
0.1%

coffee_robustas
Real number (ℝ)

Distinct715
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7274784
Minimum0.4872098
Maximum6.883547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:28.909365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4872098
5-th percentile0.66390605
Q10.92305308
median1.6321718
Q32.2822
95-th percentile3.4715457
Maximum6.883547
Range6.3963372
Interquartile range (IQR)1.3591469

Descriptive statistics

Standard deviation0.94074785
Coefficient of variation (CV)0.54457866
Kurtosis2.5466008
Mean1.7274784
Median Absolute Deviation (MAD)0.69015937
Skewness1.1896824
Sum1305.9736
Variance0.88500652
MonotonicityNot monotonic
2023-11-15T22:50:29.199187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6684254 5
 
0.7%
0.6723936 4
 
0.5%
0.6968643 3
 
0.4%
0.6765823 3
 
0.4%
0.9298873 3
 
0.4%
0.6117679 3
 
0.4%
0.6157362 3
 
0.4%
0.9400282 3
 
0.4%
2.041257658 2
 
0.3%
0.6887074 2
 
0.3%
Other values (705) 725
95.9%
ValueCountFrequency (%)
0.4872098 1
0.1%
0.5029 1
0.1%
0.5124 1
0.1%
0.5221 1
0.1%
0.5351 1
0.1%
0.5368 1
0.1%
0.5373 1
0.1%
0.5469536 1
0.1%
0.5692 1
0.1%
0.5798017 1
0.1%
ValueCountFrequency (%)
6.883547 1
0.1%
6.747966 1
0.1%
5.942636 1
0.1%
5.431397 1
0.1%
4.938676 1
0.1%
4.767601 1
0.1%
4.496439 1
0.1%
4.482551 1
0.1%
4.442207 1
0.1%
4.331758 1
0.1%

tea_columbo
Real number (ℝ)

Distinct608
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7779616
Minimum0.4341979
Maximum4.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:29.497000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4341979
5-th percentile0.635353
Q10.8925009
median1.5040005
Q32.5152043
95-th percentile3.6025
Maximum4.49
Range4.0558021
Interquartile range (IQR)1.6227034

Descriptive statistics

Standard deviation1.0086791
Coefficient of variation (CV)0.56732334
Kurtosis-0.66875673
Mean1.7779616
Median Absolute Deviation (MAD)0.6576996
Skewness0.74423496
Sum1344.139
Variance1.0174335
MonotonicityNot monotonic
2023-11-15T22:50:30.036667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9303009 12
 
1.6%
0.8610009 12
 
1.6%
0.8148008 12
 
1.6%
0.8190008 12
 
1.6%
0.8463009 12
 
1.6%
0.7602008 12
 
1.6%
0.6770413 12
 
1.6%
0.5964011 12
 
1.6%
0.6266412 12
 
1.6%
0.8925009 12
 
1.6%
Other values (598) 636
84.1%
ValueCountFrequency (%)
0.4341979 1
 
0.1%
0.5221966 1
 
0.1%
0.5455723 1
 
0.1%
0.5837871 1
 
0.1%
0.5846411 1
 
0.1%
0.5964011 12
1.6%
0.6023916 1
 
0.1%
0.6031651 1
 
0.1%
0.6047198 1
 
0.1%
0.617284 1
 
0.1%
ValueCountFrequency (%)
4.49 1
0.1%
4.27 1
0.1%
4.21 1
0.1%
4.19 1
0.1%
4.16 1
0.1%
4.135620511 1
0.1%
4.13 1
0.1%
4.11 2
0.3%
4.1 1
0.1%
4.09 1
0.1%

tea_kolkata
Real number (ℝ)

Distinct634
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8703082
Minimum0.6647995
Maximum4.0730112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:30.355472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6647995
5-th percentile0.7986667
Q11.2973687
median1.8506122
Q32.3768987
95-th percentile3.0886475
Maximum4.0730112
Range3.4082117
Interquartile range (IQR)1.07953

Descriptive statistics

Standard deviation0.69786695
Coefficient of variation (CV)0.3731294
Kurtosis-0.55790982
Mean1.8703082
Median Absolute Deviation (MAD)0.53728308
Skewness0.26941242
Sum1413.953
Variance0.48701829
MonotonicityNot monotonic
2023-11-15T22:50:30.642294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.121401 12
 
1.6%
0.7986667 12
 
1.6%
0.978601 12
 
1.6%
0.9906667 12
 
1.6%
0.7333334 12
 
1.6%
0.8626667 12
 
1.6%
1.142401 12
 
1.6%
1.039501 12
 
1.6%
1.083601 12
 
1.6%
1.104601 12
 
1.6%
Other values (624) 636
84.1%
ValueCountFrequency (%)
0.6647995 1
 
0.1%
0.6855978 1
 
0.1%
0.7084 1
 
0.1%
0.7098448 1
 
0.1%
0.7105354 1
 
0.1%
0.7253865 1
 
0.1%
0.7323374 1
 
0.1%
0.7333334 12
1.6%
0.7353197 1
 
0.1%
0.7530807 1
 
0.1%
ValueCountFrequency (%)
4.073011154 1
0.1%
3.990422346 1
0.1%
3.957038 1
0.1%
3.856839612 1
0.1%
3.550720905 1
0.1%
3.538153514 1
0.1%
3.504758045 1
0.1%
3.469254 1
0.1%
3.412294528 1
0.1%
3.351542111 1
0.1%

tea_mombasa
Real number (ℝ)

Distinct531
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6712216
Minimum0.7195997
Maximum3.3925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:30.932117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.7195997
5-th percentile0.8357997
Q11.1368
median1.5982575
Q32.0838297
95-th percentile2.874375
Maximum3.3925
Range2.6729003
Interquartile range (IQR)0.94702967

Descriptive statistics

Standard deviation0.61535745
Coefficient of variation (CV)0.36820818
Kurtosis-0.47147177
Mean1.6712216
Median Absolute Deviation (MAD)0.4614575
Skewness0.53555512
Sum1263.4435
Variance0.37866479
MonotonicityNot monotonic
2023-11-15T22:50:31.237928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0374 12
 
1.6%
0.8357997 12
 
1.6%
0.9841996 12
 
1.6%
1.1368 12
 
1.6%
0.8105997 12
 
1.6%
0.8875996 12
 
1.6%
0.7195997 12
 
1.6%
0.9015996 12
 
1.6%
0.9911996 12
 
1.6%
0.9141996 12
 
1.6%
Other values (521) 636
84.1%
ValueCountFrequency (%)
0.7195997 12
1.6%
0.8105997 12
1.6%
0.8203997 5
0.7%
0.8357997 12
1.6%
0.8492754 7
0.9%
0.8875996 12
1.6%
0.9015996 12
1.6%
0.9141996 12
1.6%
0.9225996 12
1.6%
0.9533996 12
1.6%
ValueCountFrequency (%)
3.3925 1
0.1%
3.268 1
0.1%
3.24 1
0.1%
3.17 1
0.1%
3.1375 1
0.1%
3.1025 1
0.1%
3.0925 1
0.1%
3.0825 1
0.1%
3.077708 1
0.1%
3.076666667 1
0.1%

sugar_eu
Real number (ℝ)

Distinct592
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4051577
Minimum0.11221516
Maximum0.78317064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:31.527747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.11221516
5-th percentile0.11221516
Q10.29811974
median0.40234315
Q30.56951938
95-th percentile0.6808
Maximum0.78317064
Range0.67095548
Interquartile range (IQR)0.27139964

Descriptive statistics

Standard deviation0.18774134
Coefficient of variation (CV)0.46337844
Kurtosis-1.0493253
Mean0.4051577
Median Absolute Deviation (MAD)0.14906998
Skewness-0.16765975
Sum306.29922
Variance0.035246812
MonotonicityNot monotonic
2023-11-15T22:50:31.807574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.112215158 43
 
5.7%
0.12676565 23
 
3.0%
0.12456103 13
 
1.7%
0.12235641 12
 
1.6%
0.129411194 12
 
1.6%
0.124340568 12
 
1.6%
0.130954428 12
 
1.6%
0.126324726 12
 
1.6%
0.295639542 4
 
0.5%
0.40234315 3
 
0.4%
Other values (582) 610
80.7%
ValueCountFrequency (%)
0.112215158 43
5.7%
0.113758392 1
 
0.1%
0.115522088 1
 
0.1%
0.116403936 1
 
0.1%
0.116624398 1
 
0.1%
0.118167632 1
 
0.1%
0.12235641 12
 
1.6%
0.124340568 12
 
1.6%
0.12456103 13
 
1.7%
0.126324726 12
 
1.6%
ValueCountFrequency (%)
0.78317064 1
0.1%
0.7822045029 1
0.1%
0.77287176 1
0.1%
0.7726612114 1
0.1%
0.7714 1
0.1%
0.7439769257 1
0.1%
0.7326995657 1
0.1%
0.7311857236 1
0.1%
0.7293000343 1
0.1%
0.7239194259 1
0.1%

sugar_us
Real number (ℝ)

Distinct595
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43246218
Minimum0.11684486
Maximum1.2632473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:32.118384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.11684486
5-th percentile0.13007258
Q10.2976237
median0.4711192
Q30.51218834
95-th percentile0.78043548
Maximum1.2632473
Range1.1464024
Interquartile range (IQR)0.21456464

Descriptive statistics

Standard deviation0.18858876
Coefficient of variation (CV)0.43608151
Kurtosis0.11824936
Mean0.43246218
Median Absolute Deviation (MAD)0.081319197
Skewness0.10340297
Sum326.94141
Variance0.035565721
MonotonicityNot monotonic
2023-11-15T22:50:32.449179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12786796 14
 
1.9%
0.14991416 9
 
1.2%
0.12345872 8
 
1.1%
0.13007258 8
 
1.1%
0.13889106 8
 
1.1%
0.1543234 7
 
0.9%
0.15873264 7
 
0.9%
0.1322772 7
 
0.9%
0.13668644 7
 
0.9%
0.14770954 7
 
0.9%
Other values (585) 674
89.2%
ValueCountFrequency (%)
0.11684486 1
 
0.1%
0.11904948 1
 
0.1%
0.1212541 4
 
0.5%
0.12345872 8
1.1%
0.12566334 4
 
0.5%
0.12786796 14
1.9%
0.13007258 8
1.1%
0.1322772 7
0.9%
0.13448182 3
 
0.4%
0.13668644 7
0.9%
ValueCountFrequency (%)
1.26324726 1
0.1%
1.028234768 1
0.1%
0.91932654 1
0.1%
0.887405963 1
0.1%
0.8852599119 1
0.1%
0.88515493 1
0.1%
0.8802568395 1
0.1%
0.8750903604 1
0.1%
0.873911368 1
0.1%
0.867692019 1
0.1%

sugar_world
Real number (ℝ)

Distinct694
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24026341
Minimum0.0287
Maximum1.2377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-11-15T22:50:32.741001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0287
5-th percentile0.048275
Q10.13970468
median0.21528546
Q30.309325
95-th percentile0.51980635
Maximum1.2377
Range1.209
Interquartile range (IQR)0.16962032

Descriptive statistics

Standard deviation0.15194696
Coefficient of variation (CV)0.63241824
Kurtosis4.3226874
Mean0.24026341
Median Absolute Deviation (MAD)0.083338956
Skewness1.4836942
Sum181.63914
Variance0.02308788
MonotonicityNot monotonic
2023-11-15T22:50:33.052805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037 4
 
0.5%
0.0666 3
 
0.4%
0.2641 3
 
0.4%
0.0805 3
 
0.4%
0.0626 3
 
0.4%
0.0337 2
 
0.3%
0.1788 2
 
0.3%
0.0452 2
 
0.3%
0.189376858 2
 
0.3%
0.1726 2
 
0.3%
Other values (684) 730
96.6%
ValueCountFrequency (%)
0.0287 1
 
0.1%
0.0298 1
 
0.1%
0.0304 1
 
0.1%
0.0311 1
 
0.1%
0.0329 1
 
0.1%
0.0337 2
0.3%
0.0346 1
 
0.1%
0.0359 2
0.3%
0.0362 1
 
0.1%
0.037 4
0.5%
ValueCountFrequency (%)
1.2377 1
0.1%
0.9894 1
0.1%
0.894 1
0.1%
0.8708 1
0.1%
0.8445 1
0.1%
0.833 1
0.1%
0.7649 1
0.1%
0.7529 1
0.1%
0.7491 1
0.1%
0.7028 1
0.1%

Interactions

2023-11-15T22:50:23.128931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:57.373876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:00.384015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:04.291496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:06.674023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:09.395340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:12.133650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:15.203758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:17.691287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:20.475569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:23.413755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:57.920535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:00.651851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:04.548340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:06.979833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:09.810091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:12.372502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:15.456599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:18.147004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:20.702428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:23.676591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:58.231345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:01.027510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:04.785189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:07.256663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:10.052936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:12.623349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:15.707443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:18.381864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:20.947276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:23.946426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:58.473196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:01.271359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:05.014046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:07.568468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:10.300783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:12.915170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:15.939308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:18.624717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:21.213115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:24.215262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:58.856956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:02.132825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:05.236914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:07.827314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:10.528642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:13.549777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:16.166340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:18.850574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:21.471951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:24.504081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:59.093812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:02.660500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:05.515739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:08.120131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:10.799474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:13.852589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:16.430065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:19.087425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:21.766771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:24.779910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:59.341658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:03.154197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:05.767581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:08.388964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:11.090294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:14.111431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:16.656927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:19.539149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:22.043602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:25.040749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:59.677450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:03.462013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:05.997440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:08.638809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:11.332151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:14.352282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:16.866797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:19.770004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:22.303438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:25.348562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:49:59.922300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:03.762820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:06.227300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:08.880666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:11.611980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:14.656095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:17.091657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:20.011854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:22.574270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:25.611398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:00.158154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:04.015670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:06.447169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:09.130507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:11.871815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:14.906943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:17.337510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:20.243717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-11-15T22:50:22.863096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-11-15T22:50:33.310646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
oil_brentoil_dubaicoffee_arabicacoffee_robustastea_columbotea_kolkatatea_mombasasugar_eusugar_ussugar_world
oil_brent1.0000.9980.6850.4820.8460.7350.8350.5290.7100.689
oil_dubai0.9981.0000.6930.4940.8460.7350.8390.5200.7120.689
coffee_arabica0.6850.6931.0000.8600.6410.7320.7740.3470.6420.616
coffee_robustas0.4820.4940.8601.0000.3680.5990.5780.2490.4590.462
tea_columbo0.8460.8460.6410.3681.0000.7580.8550.4910.7520.612
tea_kolkata0.7350.7350.7320.5990.7581.0000.7880.4150.7110.566
tea_mombasa0.8350.8390.7740.5780.8550.7881.0000.4010.6940.601
sugar_eu0.5290.5200.3470.2490.4910.4150.4011.0000.4850.379
sugar_us0.7100.7120.6420.4590.7520.7110.6940.4851.0000.777
sugar_world0.6890.6890.6160.4620.6120.5660.6010.3790.7771.000

Missing values

2023-11-15T22:50:26.051124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-15T22:50:26.451879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dateoil_brentoil_dubaicoffee_arabicacoffee_robustastea_columbotea_kolkatatea_mombasasugar_eusugar_ussugar_world
01960-01-011.631.630.94090.6968640.9303011.1214011.03740.1223560.1168450.0666
11960-02-011.631.630.94690.6887070.9303011.1214011.03740.1223560.1190490.0679
21960-03-011.631.630.92810.6887070.9303011.1214011.03740.1223560.1212540.0683
31960-04-011.631.630.93030.6845190.9303011.1214011.03740.1223560.1234590.0681
41960-05-011.631.630.92000.6906920.9303011.1214011.03740.1223560.1212540.0683
51960-06-011.631.630.91230.6968640.9303011.1214011.03740.1223560.1256630.0666
61960-07-011.631.630.91600.6906920.9303011.1214011.03740.1223560.1322770.0728
71960-08-011.631.630.92920.6988480.9303011.1214011.03740.1223560.1278680.0741
81960-09-011.631.630.92260.7028170.9303011.1214011.03740.1223560.1322770.0725
91960-10-011.631.630.92370.7067850.9303011.1214011.03740.1223560.1300730.0538
dateoil_brentoil_dubaicoffee_arabicacoffee_robustastea_columbotea_kolkatatea_mombasasugar_eusugar_ussugar_world
7462022-03-01115.59113.115.6985022.2888363.3500001.9559462.5350000.3598360.8009380.419980
7472022-04-01105.78102.685.8541482.2910414.0900003.1070042.5250000.3534690.8135050.433428
7482022-05-01112.37108.325.7412712.2729633.7100002.8690832.3766670.3454680.8020410.428799
7492022-06-01120.08115.736.0338242.2886163.5100003.2728322.1100000.3453050.7930020.417775
7502022-07-01108.92106.485.6391972.2121164.0100003.5507212.3650000.3326010.7678690.402784
7512022-08-0198.6097.755.9178612.4173664.2100003.5381542.3600000.3307730.7821990.393525
7522022-09-0190.1690.635.8971382.4550654.4900003.1531982.3600000.3236210.7709560.390659
7532022-10-0193.1390.595.2928522.2709794.1356212.8331122.4575000.3209430.7625780.386911
7542022-11-0191.0786.284.7154622.0412583.8315282.8499792.4900000.3329930.7923400.407414
7552022-12-0180.9076.784.6294822.0454463.9940732.4215162.3866670.3457290.8051270.417335